By Gopi Krishna Tummala
Act 0: MLOps in Plain English
Most people think building an AI company is about having the smartest “Brain” (The Model). But if you have a genius brain inside a body that can’t move, has no blood flow, and can’t eat, that genius is useless.
MLOps Infrastructure is the body.
- Data DNA: The food and how it’s digested.
- Dataloaders: The veins and arteries moving nutrients to the muscles.
- Training Frameworks: The nervous system coordinating thousands of muscles (GPUs) at once.
- Post-Training: Sculpting the raw brain into a specialized professional.
- Serving: The reflexes and speech that allow the brain to interact with the world.
- Kernels: Hand-grinding custom tools for impossible tasks.
- Agentic AI: Giving the body hands and autonomy to act in the world.
If your infrastructure is weak, you spend $40,000 on a GPU just to watch it sit idle. This roadmap is how you build a body strong enough to support a super-intelligence.
The Infrastructure-First Philosophy
Every production AI system is built on seven layers. This roadmap starts at the foundation and builds upward. Why? Because data format choices made in Module 1 determine whether your Module 5 serving system can scale.
graph TD
subgraph "The MLOps Stack"
L7[Module 7: Agentic MLOps - Hands & Agency]
L6[Module 6: Custom Kernels - Talking to Silicon]
L5[Module 5: LLM Serving - Reflexes]
L4[Module 4: Post-Training - Specialization]
L3[Module 3: Training Frameworks - Coordination]
L2[Module 2: Dataloaders - The Pump]
L1[Module 1: Data DNA - The Foundation]
end
L1 --> L2
L2 --> L3
L3 --> L4
L4 --> L5
L5 --> L6
L6 --> L7
style L1 fill:#d1fae5,stroke:#059669
style L7 fill:#ecfdf5,stroke:#059669The 7-Module Learning Path
📦 Module 1: The DNA of Data (Storage & Pipelines)
The Core Idea: Store in Parquet, Compute in Arrow. Learn how modern systems like HuggingFace Datasets handle petabytes of data by understanding the fundamental trade-off between storage efficiency (Parquet) and compute speed (Arrow).
📖 Deep Dive: Parquet, Arrow, and Speed
🔄 Module 2: Datasets & Dataloaders (The Intake Valve)
The Core Idea: The GPU should never wait for the CPU. Understand the “Zero-Copy” pipeline that moves data from S3 to GPU registers. Master prefetching, pinned memory, and hardware-accelerated decoding.
⚡ Module 3: Training Frameworks & Resilience (The Scale-Out)
The Core Idea: 3D Parallelism (Data, Pipeline, Tensor). Learn how to train models that don’t fit on a single GPU. Master ZeRO (Zero Redundancy Optimizer) and build clusters that survive node failures without losing progress.
🎨 Module 4: Post-Training (PEFT & Alignment)
The Core Idea: Sculpting Intelligence. Pre-training is just the start. Learn how to use LoRA, DoRA, and DPO to turn a raw model into a specialized, safe, and controllable assistant without spending millions on compute.
📖 Deep Dive: Sculpting Intelligence
🚀 Module 5: The Serving Infrastructure (vLLM & Inference)
The Core Idea: Memory is the bottleneck, not compute. Deep dive into PagedAttention and Continuous Batching. Learn how to fit 10x more users on the same GPU by managing the KV cache like an Operating System.
📖 Deep Dive: vLLM and the Trilogy of Speed
⚙️ Module 6: Custom Kernels & GPU Optimization (The Deep Tech)
The Core Idea: Hand-grinding your own tools. When standard libraries fail, we write Triton or CUDA. Learn why FlashAttention was a breakthrough in I/O management and how to bypass the “Memory Wall.”
📖 Deep Dive: Handcrafting GPU Speed
🤖 Module 7: Agentic MLOps (Beyond Inference)
The Core Idea: Giving the AI Hands. The shift from stateless request-response to stateful, autonomous agents. Master the Model Context Protocol (MCP) and secure execution sandboxes (Firecracker/gVisor).
📖 Deep Dive: Beyond Stateless Inference
The Interview Philosophy: “Why” Over “What”
Senior ML infrastructure interviews don’t test whether you know what PagedAttention is. They test whether you understand why it was necessary.
Throughout this roadmap, focus on the Trade-offs:
- Data: Why Parquet? → Trade CPU cycles for Disk I/O.
- Training: Why FSDP? → Trade Network Bandwidth for VRAM capacity.
- Serving: Why Quantization? → Trade Accuracy for Throughput.
- Kernels: Why Fusion? → Trade logic complexity for Memory Bandwidth.
Ready to Start?
Begin with Module 1: The DNA of Data. Master the foundation, and the rest of the stack becomes intuitive.
The journey from “I can train a model” to “I can build the infrastructure that powers AI at scale” starts here.